Shared Sensor Network (SSN) refers to a scenario where the same sensing and communication resources are shared and queried by multiple Internet applications. Due to the burgeoning growth in Internet applications, multiple application queries can exhibit overlapping in their functional requirements, such as the region of interest, sensing attributes, and sensing time duration. This overlapping results in redundant sensing tasks generation leading to the increased overall network traffic and energy consumption. Existing approaches operate on data sharing among various tasks to minimize the upstream traffic. However, no existing work attempts to prevent the redundant task generation to reduce the downstream traffic. Moreover, the allocation of suitable sensor nodes to meet the Quality of Service (QoS) requirements of the queries is still an open issue. This article proposes an end-to-end query processing framework (named, QueryPM) that first, calculates the functional requirements similarity among queries to prevent the redundant task generation. Then, it takes the QoS and functional requirements into account while allocating the tasks on the sensor nodes. Extensive simulations on the proposed approach show that downstream traffic, upstream traffic, and energy consumption reduced to 60%, 20--40%, and 40%, respectively, as compared to state-of-the-art mechanisms.

中文翻译：

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共享传感器网络中高效网络资源利用的查询处理框架

共享传感器网络（SSN）是指相同的传感和通信资源被多个互联网应用程序共享和查询的场景。由于 Internet 应用程序的快速增长，多个应用程序查询可能会在其功能要求上表现出重叠，例如感兴趣的区域、感知属性和感知持续时间。这种重叠导致产生冗余传感任务，导致整体网络流量和能源消耗增加。现有方法在各种任务之间进行数据共享以最小化上游流量。然而，没有现有的工作试图阻止冗余任务生成以减少下游流量。而且，分配合适的传感器节点以满足查询的服务质量 (QoS) 要求仍然是一个悬而未决的问题。本文提出了一种端到端的查询处理框架（命名为QueryPM），首先计算查询之间的功能需求相似度，以防止产生冗余任务。然后，它在分配传感器节点上的任务时考虑 QoS 和功能要求。对所提出方法的广泛模拟表明，与最先进的机制相比，下行流量、上行流量和能耗分别减少了 60%、20--40% 和 40%。它在分配传感器节点上的任务时考虑了 QoS 和功能要求。对所提出方法的广泛模拟表明，与最先进的机制相比，下行流量、上行流量和能耗分别减少了 60%、20--40% 和 40%。它在分配传感器节点上的任务时考虑了 QoS 和功能要求。对所提出方法的广泛模拟表明，与最先进的机制相比，下行流量、上行流量和能耗分别减少了 60%、20--40% 和 40%。